RMSampler: Sparse Radio Map Reconstruction with Active Sampling and Diffusion Posterior Correction

Abstract

Reconstructing a radio map (RM) from sparse received-power measurements supports non-cooperative interference monitoring and spectrum management. However,sparse measurements leave uncertainty about both the global RM and the interfering emitter location, making informative sampling and accurate reconstruction challenging. To address these challenges, a novel joint sampling and reconstruction method named RMSampler is proposed in this paper to improve measurement selection and RM reconstruction under limited measurement budgets. For measurement selection, RMSampler evaluates global RM error reduction using the covariance of diffusion-generated RMs and localization error reduction using candidate emitter locations. Because these errors have different units, their expected relative reductions are combined into a scale-invariant sampling criterion, with each acquired measurement updating both uncertainty estimates. After sampling, the updated emitter-location probabilities guide a separate conditional diffusion ensemble for final reconstruction. To improve measurement agreement, an effective sample size (ESS) target guides robust posterior correction, followed by a minimum-distortion projection enforcing measurement and building constraints. For the reconstruction ensemble, analysis establishes that increasing likelihood power reduces expected robust measurement loss, while pretrained neural parameters and architecture remain unchanged. Experiments using simulated and real-world measured data demonstrate improved reconstruction under matched measurement budgets. In the Chongqing real-world measured data experiment, root mean square error (RMSE) decreases from 3.54 to 2.73~dB relative to random sampling under a common reconstruction method.